Dimensional Defects Research Articles

Advancing smart transportation: A review of computer vision and photogrammetry in learning-based dimensional road pavement defect detection

Advancing smart transportation: A review of computer vision and photogrammetry in learning-based dimensional road pavement defect detection

Different Dimensional MOFs-Derived Defect Engineering for Highly Selective Electrocatalytic Reduction.

Approximately 4% of global carbon dioxide emissions originate from methane-to-hydrogen technologies used in industrial hydrogenation reactions. Therefore, electrocatalytic hydrogenation (ECH) technologies utilizing green hydrogen sources are gradually receiving widespread attention. How to inhibit the hydrogen evolution reaction (HER) by regulating the microenvironment in order to enhance the ECH efficiency is of great importance for environmental protection and sustainable industrial development. In this study, the in situ spatial dimension control strategy is utilized to modulate the growth of Cobalt-based metal-organic frameworks (Co-MOFs) with varied dimensions on copper foam (CF), thereby regulating the vacancy defects in the carriers to optimize the electronic state of the active sites. Notably, the catalyst derived from two-dimensional (2D Co-ZIF-L with abundant pyridinic-N vacancy defects exhibits excellent selectivity (82%) and high faradaic efficiency (FE, 66%) in the selective ECH of biomass molecules. In addition, uncovering the differences in the electronic states of active sites is key to achieving targeted adsorption and activation of reaction sites in ECH. Rationally selecting MOF-derived catalysts with different dimensions provides an effective way to regulate the microenvironment of metal nanoparticles (NPs).

The transformational power of smart factory technologies: A comprehensive analysis of quality improvement and technological innovation applying Blavaan and Bayesian SEM

Innovative concepts such as robotics, IoT, 3-D printing, and artificial intelligence (AI), which have been commonly referred to as smart factory technologies, define novel shifts in the manufacturing paradigm. The objective of this research is to assess what effect these technologies have on manufacturing processes, particularly in terms of quality improvement or technological advancement. This research integrates empirical data and a thorough literature review by applying Bayesian structural equation modeling (BSEM) with Blavaan and AMOS, as well as evaluative factor analysis. The views of 33 experts were collected on production speed, resource utilization, labor cost, quality transformation, and technical advancement through a structured survey. In this empirical analysis, we identify that smart factory technologies significantly increase production efficiency by improving resource utilization and production rate. From the standpoint of quality, the application of IoT sensors and AI-powered inspection systems enhances dimensional quality, material quality, and defect identification. 3-D printing also spurs conceptual and product prototyping, enabling shorter innovation cycles and efficient, data-driven design. The inclusion of Bayesian SEM increases the stability of these results by incorporating measurement uncertainty to deliver a more precise picture of smart technology adoption consequences. This study contributes to both theoretical and practical knowledge by demonstrating how Bayesian modeling can provide a structured framework for analyzing smart factory technologies’ effects on efficiency, quality management, and innovation. The findings offer actionable insights for manufacturers seeking to optimize production processes and align with Industry 4.0 advancements.

Development of eDART based online diameter prediction systems in injection molding with multiple linear regression and fuzzy logic

Abstract Injection molding is a versatile technique for processing a wide range of thermoplastic and thermosetting polymers, as well as their composites. Dimensional defects are a critical issue in injection molding. This research focuses on developing online diameter prediction systems via multiple linear regression (MLR) and fuzzy logic. The systems are developed using Delrin 311 DP material, with the Taguchi methodology employed to define optimized process parameters that ensure adequate process capability. Processing data were collected from the sensors embedded in the surface of mold cavity by the eDART system. Regression analysis was employed to build and test the relationship between the real-time data from in-mold sensors and the diameter of molded part. The real time data from the sensor-based monitoring system, including end of cavity, hydraulic injection pressure, and efficient viscosity, were selected as the inputs for the predictive model. Both MLR and fuzzy logic models were established to predict the outcomes, based on the data retrieved from the sensors, achieving the prediction accuracies of 99.09% for MLR and 99.98% for fuzzy logic, respectively. Fuzzy logic demonstrated its reliability in predicting diameters and minimizing dimensional defects in injection molding.

In-depth analysis of sintering, exposure time, and layer height (um) in LRS 3D printed devices with DLP

In-depth analysis of sintering, exposure time, and layer height (um) in LRS 3D printed devices with DLP

Processing Parameter Setting Procedure for a Commercial Bowden Tube FDM Printer

Additive manufacturing (AM), especially fused deposition modeling (FDM), has experienced great development and diffusion during recent years. However, it still faces some limitations, such as poor dimensional accuracy or surface defects, the improvement of which motivates the elaboration of the present work. Contrary to an approach based on the optimization of parameters to obtain a single invariant value, the main objective of this study is the design of a procedure that anyone can follow to generate a printing profile for their specific FDM printer, environment, and imposed constraints through the adjustment of some selected parameters in the popular slicing software UltiMaker Cura. The resulting procedure consists of four ad hoc designed specimens and their analysis algorithms, all connected by a general workflow that ensures the correct execution of the procedure. Its applicability and effectiveness have been proved in a case study where a printing profile was developed for the real manufacturing project of a custom 3D object in polylactic acid (PLA), obtaining an improvement of 50% in tolerances and proving that the proposed parameter setting procedure represents a reduction in the setting time and material consumption versus conventional trial and error methodologies.

Synergistic strategy via modulated energy filtering and hierarchical defects evolution for high performance GeTe thermoelectrics

Synergistic strategy via modulated energy filtering and hierarchical defects evolution for high performance GeTe thermoelectrics

Study of Injection Molding Process to Improve Geometrical Quality of Thick-Walled Polycarbonate Optical Lenses by Reducing Sink Marks.

This study investigates the challenges and potential of conventional injection molding for producing thick-walled optical components. The research primarily focuses on optimizing process parameters and mold design to enhance product quality. The methods include software simulations and experimental validation using polycarbonate test samples (optical lenses). Significant parameters such as melt temperature, mold temperature, injection pressure, and packing pressure were varied to assess their impact on geometric accuracy and visual properties. The results show that lower melt temperatures and higher mold temperatures significantly reduce the occurrence of dimensional defects. Additionally, the design of the gate system was found to be crucial in minimizing defects and ensuring uniform material flow. Effective packing pressure was essential in reducing volumetric shrinkage and sink marks. Furthermore, we monitored the deviation between the predicted and actual defects relative to the thickness of the sample wall. After optimization, the occurrence of obvious defects was eliminated across all sample thicknesses (lenses), and the impact of the critical defect, the sink mark on the planar side of the lens, was minimized. These findings demonstrate the substantial potential of conventional injection molding to produce high-quality thick-walled parts when these parameters are precisely controlled. This study provides valuable insights for the efficient design and manufacturing of optical components, addressing the growing demand for high-performance thick-walled plastic products.

Investigation on high-precision drilling of Cf/SiC composites with brazed diamond core-drill

Investigation on high-precision drilling of Cf/SiC composites with brazed diamond core-drill

Clinical and Histological Efficacy of Digitally Designed CAD CAM Allogenic Bone Blocks. A Prospective Cohort.

The growth in bone reconstructive surgery has been unsurpassed in recent decades. However, most bone regenerative products lack any potential for delivering site-specific morphologically driven augmentation. It was therefore the purpose of this study to evaluate the histological and clinical incorporation of a novel CAD CAM allogenic block bone graft for the reconstruction of complex 3- dimensional alveolar defects. In addition the clinical outcome of dental implants subsequently placed and loaded within these grafts was assessed with up to 5 years in function. Results demonstrated that 4 of the initial 17 blocks failed (23.5%). The remaining 13 blocks plus an additional two replacement blocks were fully or partially incorporated within the recipient bone site (79%). Of the 29 implants placed within the integrated blocks, no failures occurred with up to 5 years in function, with a recorded mean marginal bone loss by implant of -0.5mm.

A Detailed Properties Comparison of an Automotive Sealant Nozzle Produced Using Three Metal Additive Manufacturing Technologies.

Choosing the right metal AM equipment and material is a highly intricate process that forms a crucial part of every manufacturing company's strategic plan. This study undertakes a comprehensive comparison of the performance and material properties of three Metal Additive Manufacturing (AM) technologies: Powder Bed Fusion (PBF), Metal Filament Deposition Modeling (MFDM), and Bound Metal Deposition (BMD). An automotive nozzle was selected and manufactured using all three technologies and three metallic materials to understand their respective advantages and disadvantages. The samples were then subjected to a series of tests and evaluations, including dimensional accuracy, mechanical properties, microstructure, defects, manufacturability, and cost efficiency. The nozzle combinations were PBF in aluminum, MFDM in stainless steel, and BMD in hard tool steel. The results underscore significant differences in functionality, material characteristics, product quality, lead time, and cost efficiency, all of which are crucial factors in making equipment investment decisions. The conclusions drawn in this paper aim to assist automotive industry equipment experts in making informed decisions about the technology and materials to use for parts with characteristics like these. Future studies will delve into other technologies, automotive components, and materials to further enhance our understanding of the application of metal AM in manufacturing.

Kaizen Implementation in the Motorcycle Tire Testing Stage with the PDCA Cycle

The research was conducted at the tire trial stage at a motorcycle tire production company, which aims to reduce defects in the measurement results at the tire sample trial stage. The company policy stipulates that tire trials are carried out on 10 tire samples taken at random from each of the total production of 250 motorcycle tires. From the samples taken, it was found that the types of tire defects were Cut Sample and Crown Sample as well as tire dimension defects that did not match the target with dimensional defect types namely Pattern Bare and Bead Crack with a total of 60% defects. The method used for improvement with Kaizen through the PDCA cycle, shows several improvement steps based on Fishbone Diagram and 5W1H analysis. The result of this research is the reduction of defects to zero defects (0%), which means that the company's quality objectives can be achieved. With the principle of Kaizen through this PDCA cycle, the company gets very satisfying results, in line with its target of zero defects.

Effect of Heat Treatment on Hygroscopicity of Chinese Fir (Cunninghamia lanceolata [Lamb.] Hook.) Wood

Chinese fir (Cunninghamia lanceolata [Lamb.] Hook.) is a widely planted species of plantation forest in China, and heat treatment can improve its dimensional stability defects and improve its performance. The wood samples were heat-treated at various temperatures (160, 180, 200, and 220 °C) for 2 h. To clarify the effect of heat treatment on wood hygroscopicity, the equilibrium moisture content (EMC) was measured, the moisture adsorption and desorption rates were determined, the hygroscopic hysteresis was examined, and the Guggenheim, Anderson, and de Boer (GAB) model was fitted to the experimental data. The moisture absorption isotherms of all samples belonged to the Type II adsorption isotherm, but the shape of the desorption isotherm was more linear for heat-treated wood samples, especially when the heat treatment temperature was higher. According to the results analyzed with ANOVA, there were significant differences in equilibrium moisture content between the control samples and the heat-treated samples under the conditions of 30%, 60%, and 95% relative humidity (RH, p < 0.05), and the results of multiple comparisons were similar. The decrease in hygroscopicity was more pronounced in wood treated at higher temperatures. The EMC of the 160–220 °C heat-treated samples of the control samples was 14.00%, 22.37%, 28.95%, and 39.63% lower than that of the control sample at 95% RH. Under low RH conditions (30%), water is taken up mainly via monolayer sorption, and multilayer sorption gradually predominates over monolayer sorption with the increase in RH. The dynamic vapor sorption (DVS) analysis indicated that the heat-treated wood revealed an increase in isotherm hysteresis, which was due to the change in cell wall chemical components and microstructure caused by heat treatment. In addition, the effective specific surface area of wood samples decreased significantly after heat treatment, and the change trend was similar to that of equilibrium moisture content.

Dimensional Characterization and Hybrid Manufacturing of Copper Parts Obtained by Atomic Diffusion Additive Manufacturing, and CNC Machining.

The combination of Atomic Diffusion Additive Manufacturing (ADAM) and traditional CNC machining allows manufacturers to leverage the advantages of both technologies in the production of functional metal parts. This study presents the methodological development of hybrid manufacturing for solid copper parts, initially produced using ADAM technology and subsequently machined using a 5-axis CNC system. The ADAM technology was dimensionally characterized by adapting and manufacturing the seven types of test artifacts standardized by ISO/ASTM 52902:2019. The results showed that slender geometries suffered warpage and detachment during sintering despite complying with the design guidelines. ADAM technology undersizes cylinders and oversizes circular holes and linear lengths. In terms of roughness, the lowest results were obtained for horizontal flat surfaces, while 15° inclined surfaces exhibited the highest roughness due to the stair-stepping effect. The dimensional deviation results for each type of geometry were used to determine the specific and global oversize factors necessary to compensate for major dimensional defects. This also involved generating appropriate over-thicknesses for subsequent CNC machining. The experimental validation of this process, conducted on a validation part, demonstrated final deviations lower than 0.5% with respect to the desired final part, affirming the feasibility of achieving copper parts with a high degree of dimensional accuracy through the hybridization of ADAM and CNC machining technologies.

Minimizing Dimensional Defects in FFF Using a Novel Adaptive Slicing Method Based on Local Shape Complexity

Additive Manufacturing (AM) has emerged as an innovative technology that gives designers several advantages, such as geometric freedom of design and less waste. However, the quality of the parts produced is affected by different design and manufacturing parameters, such as the part orientation, the nozzle temperature and speed, the support material, and the layer thickness. In this context, the layer thickness is considered an important AM parameter affecting the part quality and accuracy. Thus, in this paper, a new adaptative slicing method based on the cusp vector and the surface deviation is proposed with the aim of minimizing the dimensional defects of FFF printed parts and investigate the impact on the dimensional part tolerancing. An algorithm is developed to automatically extract data from the STL file, select the build orientation, and detect intersection points between the initial slicing and the STL mesh. The innovation of this algorithm is exhibited via adapting the slicing according to the surface curvature based on two factors: the cusp vector and the surface deviation. The suggested slicing technique guarantees dimensional accuracy, especially for complex feature shapes that are challenging to achieve using a uniform slicing approach. Finally, a preview of the slicing is displayed, and the G-code is generated to be used by the FFF machine. The case study consists of the dimensional tolerance inspection of prototypes manufactured using the conventional and adaptive slicing processes. The proposed method’s effectiveness is investigated using RE and CMM processes. The method demonstrates its reliability through the observed potential for accuracy improvements exceeding 0.6% and cost savings of up to 4.3% in specific scenarios. This reliability is substantiated by comparing the resulting dimensional tolerances and manufacturing costs.

Selection of Tooling for Extrusion of Polyamide Hoses Using Process Capability Analysis

Objective: This research is a case study carried out in a polyamide hose factory located in Brazil. The research seeks to identify the best set of tools to be used in a polymer extruder in the manufacture of polyamide 11 hoses. Search with the set of tools seeking the best production quality, without dimensional variations and in a more stable way from the beginning. Methods: By visualizing the dimensions of the product and the dimensions of the tools. To achieve this, Minitab® software is used to analyze the capacity of the production process with three different combinations of tools. The three productions were carried out with modifications to the original set of tools, one with changing the former and the last with changing the pin and extrusion die. Results and conclusion: Process capacity analysis showed that the last combination, with a former with a smaller gap in relation to the hose and with a pin and die with smaller gaps than the initial set, was more efficient, producing a hose with fewer dimensional variations. Research implications: Process capability analysis proves to be useful in assisting engineers in the development of tools to produce thermoplastic hoses, reducing dimensional variations and product defects, generating savings in material, hours worked and costs. Originality/Value: Hoses are produced not only by the oil and gas sector, but also for other purposes such as hospital use, retail and civil construction. This study will help the production of thermoplastic hoses in any segment.

Non-affine deformation analysis and 3D packing defects: A new way to probe membrane heterogeneity in molecular simulations.

Non-affine deformation analysis and 3D packing defects: A new way to probe membrane heterogeneity in molecular simulations.

Holographic CFTs on AdSd× Sn and conformal defects

We consider (d+n+1)-dimensional solutions of Einstein gravity with constant negative curvature. Regular solutions of this type are expected to be dual to the ground states of (d + n)-dimensional holographic CFTs on AdSd × Sn. Their only dimensionless parameter is the ratio of radii of curvatures of AdSd and Sn. The same solutions may also be dual to (d − 1)-dimensional conformal defects in holographic QFTd+n. We solve the gravity equations with an associated conifold ansatz, and we classify all solutions both singular and regular by a combination of analytical and numerical techniques. There are no solutions, regular or singular, with two boundaries along the holographic direction. Out of the infinite class of regular solutions, only one is diffeomorphic to AdSd+n+1 and another to AdSd × AdSn+1. For the regular solutions, we compute the on-shell action as a function of the relevant parameters.

Dynamics in between Structural and Electrical Properties of as Grown ZnO Thin Films by Thermal ALD

The mechanism behind n-type conductivity of undoped ZnO films are not understood well. One and two dimensional defects (grain boundaries, dislocations), and zero dimensional stoichiometric point defects (vacancies, self-interstitials and impurities) play a crucial role in determining the electrical properties of ZnO. All defect mechanisms are strongly controlled by the growth method and conditions. While it is more straightforward examining the one and two dimensional defects, measuring and unveiling the mechanism behind the zero dimensional point defect contribution and their sole effect on the electrical properties are challenging. This is why there has been controversial discussion of results among experimental and computational works relating physical and chemical properties of ZnO to sustainable electrical properties. In this study, to correlate the dynamics in between structural and electrical properties of ZnO grown by thermal ALD; growth temperature, DEZ and DI water precursor pulse times, DEZ/DI water precursor pulse ratio, and N2 purge time were varied. To obtain growth condition specific structural and electrical properties; XRD, AFM, profilometer, ellipsometry, XPS/CasaXPS, UV-VIS spectrometer, Hall-Effect measurements were utilized. Although, there was no strong correlation for oxygen vacancies, the contribution of hydrogen impurities, zinc interstitials and oxygen vacancies to conductivity was observed at different growth conditions. Lowest resistivity and highest average % transmittance were obtained as 6.8x10-3 ohm.cm and 92% in visible spectrum (380-700 nm), respectively.

(Invited) Interaction and Transformation of Defects during Sodium-Ion Intercalation

Non-equilibrium defects often dictate macroscopic functional properties of materials. In energy materials, dislocations and planar defects significantly modify reversibility and kinetics. Nevertheless, transient imperfections briefly appearing during ionic transport have been challenging to capture, limiting understanding of their life cycle and impact. I will present our recent operando x-ray diffraction, x-ray nanoimaging, and resonant x-ray scattering results in sodium-ion intercalation compounds. Three-dimensional coherent x-ray imaging reveals the transformation and self-healing of a metastable domain boundary, glissile dislocation loop, and stacking faults. I will also discuss structure-selective x-ray spectroscopy, which allows us to link the long-range order with redox reactions in layered oxides during intercalation. The presence of high dimensional defects in energy materials observed with operando nanoimaging suggest their abundance and highlights the importance of defect engineering for developing next-generation energy solutions.